With the increase of demand on high-speed data communication, studies on a spatial multiplexing scheme for providing a high transmission rate using limited frequency resources have been performed. In the spatial multiplexing scheme, a transmitter simultaneously transmits at least two data streams using a plurality of antennas and a receiver divides and detects data streams received through a plurality of antenna in order to increase a data transmission rate.
FIG. 1 is a block diagram of a conventional spatial multiplexer. The conventional spatial multiplexer includes a pre-processor 110, a soft decision metric calculator 120, and a log likelihood ratio (LLR) calculator 130.
The pre-processor 110 receives and pre-processes a receiving signal and a channel estimation value using, for example, QR decomposition to facilitate symbol search. The soft decision metric calculator 120 searches a set of candidates for a transmitting signal using an algorithm such as a maximum likelihood method, spear decoding, or QRm-MLD to find symbols closest to the receiving signal with respect to each bit value of a transmitting symbol (i.e., with respect to a case where a bit of the transmitting symbol is 1 and a case where a bit of the transmitting symbol is 0) and calculates a shortest metric which is a distance between each closest symbol and the receiving signal. The LLR calculator 130 calculates an LLR, i.e., a soft decision value with respect to each bit value of the transmitting symbol using metrics calculated by the soft decision metric calculator 120. Such soft decision values may be used as inputs of a channel decoder. Although many approaches have been suggested for a spatial multiplexer, they usually use the structure illustrated in FIG. 1 and have differences in candidate symbol search in terms of manifestation and performance.
There are many symbol candidate search methods. When maximum likelihood estimation is used, performance is good, but system complexity increases since the amount of calculation exponentially increases as the number of transmitting data streams increases. To search for candidate symbols in the maximum likelihood estimation, metrics for all transmitting candidate symbols are calculated and the least value is selected from the metrics in order to obtain the shortest metric for each bit value of a transmitting symbol. For instance, in case of 64 quadrature amplitude modulation (QAM) 2×2 multiple-input multiple-output system (MIMO), calculations are performed with respect to 26Δ6=4096 candidates.
Recently, studies have focused on approximate maximum likelihood estimation providing performance similar to that of the maximum likelihood estimation with less complexity. Although the approximate maximum likelihood estimation is modified in various manners according to specific search methods, usually a candidate set is reduced to a smaller partial candidate set and metric calculation and selection is performed with respect to the partial candidate set. However, there is possibility of omitting essential candidates, which causes performance deterioration. On the other hand, to obtain the performance similar to that of the maximum likelihood estimation, the size of the partial candidate set needs to be increased, which increases the amount of calculation. When candidates are sequentially searched, processing may take long time and it is difficult to use efficient methods such as parallel processing and pipelining.